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!pip install geopandas
!apt-get -qq install python-cartopy python3-cartopy
import cartopy
!pip3 install geoplot
import statsmodels.iolib.foreign as smio
from pandas import DataFrame
import pandas as pd
import geopandas as gpd
import altair as alt
import vega_datasets as data
import json
import warnings
import numpy as np
import matplotlib.pyplot as plt
import geoplot as gplt
import geoplot.crs as gcrs
warnings.filterwarnings('ignore')
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from google.colab import drive
drive.mount('/content/drive')
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cd "/content/drive/My Drive/Datavisualization"
Population aging in US¶
US population aging is proceeding slowly but clearly¶
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urls = ["https://docs.google.com/spreadsheets/d/1sUA47Q8OrH18YI4q_DOP7OWIoHX2i8kH0fk5E4lFszE/export?format=csv&gid=1048618207",
"https://docs.google.com/spreadsheets/d/18KT-PC9IXKqYL-wd5zHHzYV4K8eZ9mgUe98XaFANMQM/export?format=csv&gid=2072191966",
"https://docs.google.com/spreadsheets/d/1p_fq_sG3nYnHdOkLVglxXF2zmT1XrPK0c-538lcFIAg/export?format=csv&gid=1688370404",
"https://docs.google.com/spreadsheets/d/1ymCdsBHgamJis3_2PK0IZS_VZvls5Umn-ng9o4S0Sro/export?format=csv&gid=119942101",
"https://docs.google.com/spreadsheets/d/1AqUeUfzuAUG5pCLJGbrysCcTxICCrHsA8rRE1ffHZpU/export?format=csv&gid=1384942932",
"https://docs.google.com/spreadsheets/d/1zvfu9Nc27aGo2ovAn2kqofv-iVXO61-n-dQ4DIrpbUs/export?format=csv&gid=2105631292",
"https://docs.google.com/spreadsheets/d/1XAXIQeLrgxgEx6CCHxWOmfgmInRxwCxu-4vw1PR8dp8/export?format=csv&gid=1064535711",
"https://docs.google.com/spreadsheets/d/1xad1XOm-J4T13rYFJ7BBeskaaCV88BAQZExqNX3aX0w/export?format=csv&gid=95139586",
"https://docs.google.com/spreadsheets/d/1pBj5q5NkzpZUKeOqFZCGb0RdzV1mvS8rw2iXWcwYKbc/export?format=csv&gid=616415653",
"https://docs.google.com/spreadsheets/d/1dZBbXEKSRqhR8Yy9_zFBAjzdw_Gyu3czYT0GNcGhrZI/export?format=csv&gid=687388009",
"https://docs.google.com/spreadsheets/d/1pReietRze4QKpMOM6dbJeJ4bdekcGgrxcaC5k4Qv8C8/export?format=csv&gid=2108461693",
"https://docs.google.com/spreadsheets/d/1KkqQ8U4vL5Tjy7Cq16FQD7xi3qnfPnyYiMYQuiJ6Hkc/export?format=csv&gid=957537605"]
dfs =[]
for url in urls:
df = pd.read_csv(url, encoding='latin-1')[1:]
dfs.append(df)
pop65_conc = pd.concat(dfs)
col_use = ["date", "HC01_EST_VC01", "HC02_EST_VC01"]
pop65_conc = pop65_conc[col_use]
pop65_conc["HC01_EST_VC01"]=pop65_conc["HC01_EST_VC01"].astype("int")
pop65_conc["HC02_EST_VC01"]=pop65_conc["HC02_EST_VC01"].astype("int")
pop65_conc["pop65_rate"] = pop65_conc["HC02_EST_VC01"]/pop65_conc["HC01_EST_VC01"]
pop65_conc["date"] =pd.to_datetime(pop65_conc["date"])
pop65_conc["year"] = pop65_conc["date"].dt.year
pop65_conc = pop65_conc.drop("date", axis=1)
pd.set_option('display.max_rows', 10000)
pop65_conc["year"] = pop65_conc["year"].astype("str")
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step = 30
overlap = 0.5
alt.themes.enable('latimes')
chart = alt.Chart(pop65_conc, height=step, width=700).transform_joinaggregate(
mean_rate='mean(pop65_rate)', groupby=['year']
).transform_bin(
['bin_max', 'bin_min'], field='pop65_rate', bin=alt.BinParams(step=0.03)
).transform_aggregate(
value='count()', groupby=['year','mean_rate', 'bin_min', 'bin_max']
).transform_impute(
impute='value', groupby=['year','mean_rate'], key='bin_min', value=0
).mark_area(
interpolate='monotone',
fillOpacity=0.8,
stroke='lightgray',
strokeWidth=0.5
).encode(
alt.X('bin_min:Q', bin='binned', title='population rate of over 65',
axis=alt.Axis(title='Population rate of over age 65', ),
scale=alt.Scale(domain=(0.05, 0.30))),
alt.Y(
'value:Q',
scale=alt.Scale(range=[step, -step * overlap]),
axis= None
),
alt.Fill(
'mean_rate:Q',
legend=alt.Legend(title="Population rate of over age65"),
scale=alt.Scale(domain=[0.16, 0.11], scheme='inferno')
)
).facet(
row = alt.Row(
'year:N',
title="year",
header=alt.Header(titleFontSize=13, labelAngle=0, labelFontSize=12, labelAlign="left")
)
).properties(
title={
"text": ["US Population has been ageing clearly in last 10 years"],
"subtitle": ["Distribution of over65 population rate for each county"],
"color": "blue",
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
},
bounds='flush'
).configure_facet(
spacing=0
).configure_view(
stroke=None
).configure_title(
anchor='end'
)
config = chart.configure_title(
fontSize=24,
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=13,
titleFontWeight="bold",
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
titleFontWeight="bold",
labelFontSize=11,
grid=True,
gridDash=[2,3]
)
config
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sc00_18 = pd.read_csv("sc00_18.csv")
sc00_18_wrkag = sc00_18[(sc00_18["AGE"]>=15) & (sc00_18["AGE"]<=64)]
sc00_18_ov65 = sc00_18[(sc00_18["AGE"]>=65) & (sc00_18["AGE"]!=999)]
sc00_18_wrkag_div = sc00_18_wrkag.groupby(["DIVISION"]).sum()
sc00_18_ov65_div = sc00_18_ov65.groupby(["DIVISION"]).sum()
sc00_18_wrkag_divt =sc00_18_wrkag_div.T[8:]
div_dic = {0:"USA", 1:"New England", 2:"Mid-Atlantic", 3:"East North Central", 4:"West North Central", \
5:"South Atlantic", 6:"East South Central", 7:"West South Central", 8:"Mountain", 9:"Pacific"}
div_list = []
for i in range(0, 10):
sc00_18_wrkag_divt_col = sc00_18_wrkag_divt[i].reset_index()
year = sc00_18_wrkag_divt_col["index"].apply(lambda x: x[6:10])
sc00_18_wrkag_divt_col["index"]=year
sc00_18_wrkag_divt_col = sc00_18_wrkag_divt_col.rename(columns={"index":"year", i:"pop"})
sc00_18_wrkag_divt_col["pct_chg"] = sc00_18_wrkag_divt_col["pop"].pct_change()
sc00_18_wrkag_divt_col["pct_chg"][0] = 0
sc00_18_wrkag_divt_col["pct_chg"] = sc00_18_wrkag_divt_col["pct_chg"]*100 +100
sc00_18_wrkag_divt_col
sc00_18_wrkag_divt_col["div"] = i
div = sc00_18_wrkag_divt_col["div"].apply(lambda x: div_dic[i])
sc00_18_wrkag_divt_col["div"] = div
div_list.append(sc00_18_wrkag_divt_col)
conc_div = pd.concat(div_list)
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conc_div
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conc_div["a"] =100
box = pd.DataFrame({'x1': [2000], 'x2': [2018], 'y1': [0], 'y2': [100]})
domain = ["USA", "New England", "Mid-Atlantic","East North Central", "West North Central",\
"South Atlantic", "East South Central", "West South Central", "Mountain", "Pacific"]
range_ = ['red', 'blue', 'green','orange','pink','light-gray','light-gray','light-gray','light-gray','light-grey']
chart = alt.Chart(conc_div, width=800, height=300).mark_line().encode(
x=alt.X('year:O',
axis=alt.Axis(title="year")),
y = alt.Y('pct_chg:Q',
scale=alt.Scale(domain=(99, 103),),
axis=alt.Axis(title="percent_change(indexed to 100)")
),
color=alt.Color('div:N', scale=alt.Scale(domain=domain, range=range_),
legend=alt.Legend(title="Division",
values=["USA", "New England", "Mid-Atlantic","East North Central","West North Central",
"(light-grey)Other divisions"]))
).properties(
title={
"text": ["Working-age population starts declining in some areas"],
"subtitle": ["The change rate of working-age population for each division"],
"color": "blue",
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
}
)
box = alt.Chart(conc_div).mark_rect(fill='#75bbff', stroke='none',strokeOpacity=0, fillOpacity=0.03).encode(
alt.X('year:N'),
alt.Y('a')
)
rule = alt.Chart(conc_div).mark_rule(color='black', strokeDash=[2,3], size=2).encode(
y='a:Q'
)
config = (chart+box+rule).configure_title(
fontSize=24,
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=12,
titleFontWeight="bold",
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
labelFontSize=10,
labelAngle=-45,
grid=True,
gridDash=[2,3],
titleFontWeight="bold"
).configure_axisY(
labelFontSize=11,
grid=True,
gridDash=[2,3],
titleFontWeight="bold"
)
config
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sc00_18_rate = sc00_18_wrkag_div
for i in range(8, len(sc00_18_rate.columns)):
sc00_18_rate.iloc[:,i] = np.divide(sc00_18_rate.iloc[:,i], sc00_18_ov65_div.iloc[:, i])
div_dic = {0:"USA", 1:"New England", 2:"Mid-Atlantic", 3:"East North Central", 4:"West North Central", \
5:"South Atlantic", 6:"East South Central", 7:"West South Central", 8:"Mountain", 9:"Pacific"}
sc00_18_rate2000 = sc00_18_rate.iloc[:, 8].reset_index()
sc00_18_rate2000["DIVISION"] = sc00_18_rate2000["DIVISION"].apply(lambda x: div_dic[x])
sc00_18_rate2000 = sc00_18_rate2000.rename(columns={"DIVISION":"Division", "POPEST2000_CIV": "per_wk"})
sc00_18_rate2000["Division"][2] = "Middle Atlantic"
sc00_18_rate2018 = sc00_18_rate.iloc[:,26].reset_index()
sc00_18_rate2018["DIVISION"] = sc00_18_rate2018["DIVISION"].apply(lambda x: div_dic[x])
sc00_18_rate2018 = sc00_18_rate2018.rename(columns={"DIVISION":"Division", "POPEST2018_CIV": "per_wk"})
sc00_18_rate2018["Division"][2] = "Middle Atlantic"
sc00_18_rate2000_div = sc00_18_rate2000[1:]
sc00_18_rate2018_div = sc00_18_rate2018[1:]
div = gpd.read_file("cb_2018_us_division_500k.shp")
col_use=["NAME","geometry"]
div = div[col_use]
div = div.rename(columns={"NAME":"Division"})
gdf1 = pd.merge(div, sc00_18_rate2000_div)
div["lon_lat"]= div.centroid
div2=div.drop(["geometry"], axis=1)
div2 = div2.rename(columns={"lon_lat":"geometry"})
div2["longitude"]= div2.geometry.x
div2["latitude"]= div2.geometry.y
div3 = pd.merge(div2, sc00_18_rate2000_div)
div3["per_wk"] = div3["per_wk"]
div3["longitude"] = div3["longitude"]
div4 = pd.merge(div2, sc00_18_rate2018_div)
div4["per_wk"] = div4["per_wk"]
div4["longitude"] = div4["longitude"]
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choro_json = json.loads(gdf1.to_json())
choro_data = alt.Data(values=choro_json['features'])
base = alt.Chart(choro_data).mark_geoshape(
fill="lightgrey",
stroke='white',
strokeWidth=1
).encode(
).project(
type='albersUsa'
).properties(
title={
"text": "Lesser younger generation against elderly people",
"subtitle": ["The number of 65 over population per working-age person in 2000 and 2018"],
"color": "blue",
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
},width=800,
height=550
)
points1 = alt.Chart(div3).mark_circle(color="red", fillOpacity=0.8).encode(
latitude="latitude:Q",
longitude="longitude:Q",
size=alt.Size("per_wk:Q",scale=alt.Scale(domain=[3.5, 6.1], range=[0, 3000]),
legend=alt.Legend(title="65over/working-age(2000)")
),
tooltip=["Division:N", "per_wk:Q"]
)
points2 = alt.Chart(div4).mark_circle(color="blue", fillOpacity=0.8).encode(
latitude="latitude:Q",
longitude="longitude:Q",
size=alt.Size("per_wk:Q",scale=alt.Scale(domain=[3.5, 6.1], range=[0, 3000]),
legend=alt.Legend(title="65over/working-age(2018)")
),
tooltip=["Division:N", "per_wk:Q"]
)
points = (points1 + points2).resolve_scale(size='independent')
config = (base + points)
config = config.configure_title(
fontSize=24,
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=12,
titleFontWeight="bold",
symbolType="circle",
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
labelFontSize=10,
labelAngle=-45
).configure_axisY(
labelFontSize=10)
config
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1. Choropleth map for fertility rate per county(Revised)¶
Load data and preprocessing¶
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df = pd.read_csv("fertility_data2.csv")
county = gpd.read_file("UScounties.shp")
county = county.rename(columns={"FIPS":"geoid"})
county["geoid"]= county["geoid"].astype(int)
county = county[["geoid", "geometry"]]
gdf_fert = county.merge(df, on="geoid")
choro_json = json.loads(gdf_fert.to_json())
choro_data = alt.Data(values=choro_json['features'])
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df.iloc[1,:]["fert_13_17"]
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def evaluateForBivariate(row, x_bl, x_bh, y_bl, y_bh):
colorMatrix = [["#fd8d3c", "#843c39", "#393b79"],
["#f1e2cc", "#cccccc", "#4c78a8"],
["#ffffcc", "#c6bdef", "#3182bd"],]
xBoundaries = [x_bl, x_bh]
xIdx = 0
if row["total_pop"] < xBoundaries[0]:
xIdx = 0
elif row["total_pop"] < xBoundaries[1]:
xIdx = 1
else:
xIdx = 2
yBoundaries = [y_bl, y_bh]
yIdx = 0
if row["fert_13_17"] < yBoundaries[0]:
yIdx = 2
elif row["fert_13_17"] < yBoundaries[1]:
yIdx = 1
else:
yIdx = 0
return colorMatrix[yIdx][xIdx];
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color = []
for i in range(0, len(df)):
row = df.iloc[i,:]
color.append(evaluateForBivariate(row, pop_33, pop_66, fert_33, fert_66))
df["color"] = np.array(color)
county = gpd.read_file("UScounties.shp")
county = county.rename(columns={"FIPS":"geoid"})
county["geoid"]= county["geoid"].astype(int)
county = county[["geoid", "geometry"]]
gdf_fert = county.merge(df, on="geoid")
choro_json = json.loads(gdf_fert.to_json())
choro_data = alt.Data(values=choro_json['features'])
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gdf_fert
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pop_33 = np.percentile(df["total_pop"], 33.3)
pop_66 = np.percentile(df["total_pop"], 66.7)
fert_33 = np.percentile(df["fert_13_17"], 33.3)
fert_66 = np.percentile(df["fert_13_17"], 66.7)
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color_range = ["#fd8d3c", "#843c39", "#393b79","#f1e2cc", "#cccccc", "#4c78a8", "#ffffcc", "#c6bdef", "#3182bd"]
color_domain = ["#fd8d3c", "#843c39", "#393b79","#f1e2cc", "#cccccc", "#4c78a8", "#ffffcc", "#c6bdef", "#3182bd"]
base = alt.Chart(choro_data).mark_geoshape(
stroke='black',
strokeWidth=1
).encode(
).project(
type='albersUsa'
).properties(
title={
"text": "Fertility rate tends to be higher in rural areas",
"subtitle": ["Fertility rate per 1000 women for each county"],
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
},width=800,
height=600
)
# Add Choropleth Layer
choro = alt.Chart(choro_data).mark_geoshape().encode(
color = alt.Color("properties.color:N",
scale = alt.Scale(domain=color_domain, range=color_range),
title = "Fertility rate per 1000 women"),
tooltip=['properties.county:O','properties.fert_13_17:Q']
).project(
type='albersUsa'
)
alt.renderers.enable(embed_options={'theme': 'quartz'})
config = base+choro
config = config.configure_title(
color='blue',
fontSize=24,
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=12,
titleFontWeight="bold",
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
labelFontSize=11
).configure_axisY(
labelFontSize=11)
config
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Above is the choropleth map which shows the fertility rate per 1000 women at county level. The thickness of color expresses how high the fertility rate for each county is. It is a little difficult to read clear pattern from the map, but we still can see that for most of the counties, fertility rate is around 50 per 1000 womens, and counties with relatively high level of fertility(roughly over 100) are seen more in central and western regions.¶
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df = pd.read_csv("fertility_data2.csv")
df.head()
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base = alt.Chart(df).properties(
width=700,
height=300,
title={
"text": "Fertility rate tends to be higher in rural areas",
"subtitle": ["Fertility rate per 1000 women for each county"],
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
}
)
color_scale = alt.Scale(domain=['Midwest', 'Northeast', 'South','West', 'nan'],
range=['#98df8a', '#ff7f00','#ffed6f','#8624F5','#9d755d'])
hists = base.mark_bar(opacity=0.6, thickness=100).encode(
x=alt.X('fert_13_17',
bin=alt.Bin(maxbins=140), # step keeps bin size the same
scale=alt.Scale(domain=[0,160]),
axis=alt.Axis(title='fertility_rate')),
y=alt.Y('count()',
stack=None,
scale=alt.Scale(domain=[0,100])),
color=alt.Color('region:N',scale=color_scale)
).configure_title(
fontSize=24,
color="blue",
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=12,
titleFontWeight="bold",
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
labelFontSize=10,
grid=False,
gridDash=[2,3],
titleFontWeight="bold"
).configure_axisY(
labelFontSize=11,
grid=True,
gridDash=[2,3],
titleFontWeight="bold"
)
hists
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2.Stacked area chart for population over age of 65¶
Load and preprocess data¶
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aging = pd.read_csv("https://docs.google.com/spreadsheets/d/1ggeuLBBugr7yyctZ1qM_evCxKdBYqq_LaDRRRmklsCY/export?format=csv&gid=1073389986", encoding='latin-1')[1:]
use_cols = ["YEAR","HC01_EST_VC01","HC02_EST_VC01","HC02_EST_VC09","HC02_EST_VC10","HC02_EST_VC11",\
"HC02_EST_VC12", "HC02_EST_VC13", "HC02_EST_VC14", "HC02_EST_VC15", "HC02_EST_VC16", "HC02_EST_VC18"]
aging = aging[use_cols]
aging.columns = ["Year", "total_pop", "total_pop_65", "total_pop65_onerace", "White", "Black",\
"Native","Asian","hawai","other", "two_or_more", "Hispanic or Latino"]
aging["Year"]= aging["Year"].astype("int")
aging["White"] = aging["White"].astype("float")
aging["Black"] = aging["Black"].astype("float")
aging["Native"] = aging["Native"].astype("float")
aging["Asian"] = aging["Asian"].astype("float")
aging["hawai"] = aging["hawai"].astype("float")
aging["other"] = aging["other"].astype("float")
aging["two_or_more"] = aging["two_or_more"].astype("float")
aging["Hispanic or Latino"] = aging["Hispanic or Latino"].astype("float")
aging["Others"] = aging["hawai"] + aging["other"] + aging["two_or_more"]
aging_nonhis = aging.drop(["hawai", "other", "two_or_more","Hispanic or Latino"], axis=1)
aging_his = aging[["Year", "Hispanic or Latino"]]
aging_his["Race"] = "Hispanic or Latino"
aging_his
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white = aging_nonhis[["Year","White"]]
white["Race"]="White"
white = white.rename(columns={"White":"pop65"})
white = white.sort_values(by=["Year"])
black = aging_nonhis[["Year","Black"]]
black["Race"]="Black"
black = black.rename(columns={"Black":"pop65"})
black = black.sort_values(by=["Year"])
native = aging_nonhis[["Year","Native"]]
native["Race"]="Native"
native = native.rename(columns={"Native":"pop65"})
native = native.sort_values(by=["Year"])
asian = aging_nonhis[["Year","Asian"]]
asian["Race"]="Asian"
asian = asian.rename(columns={"Asian":"pop65"})
asian = asian.sort_values(by=["Year"])
other = aging_nonhis[["Year","Others"]]
other["Race"]="Others"
other = other.rename(columns={"Others":"pop65"})
other = other.sort_values(by=["Year"])
aging_conc_nonhis = pd.concat([white, black, native, asian, other])
Below the stacked area chart shows how the racial structure of population over age of 65 changed by the year. We can see that through entire period the majority of population over 65 years old have been white, over 72 percent. However, the proportion of white slightly have decreased and the proportion of asian has been increased little by little.¶
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col_domain = ["White", "Black", "Native", "Asian", "Others"]
col_range = ["yellow", "blue", "red", "green", "pink"]
chart1 = alt.Chart(aging_conc_nonhis).mark_area().encode(
x="Year:N",
y=alt.Y("pop65:Q",
scale=alt.Scale(domain=[0,100]),
axis=alt.Axis(title="Population of people over65(%)")
),
color=alt.Color("Race:N",
scale= alt.Scale(domain=col_domain, range=col_range))
).properties(
width=500,
height=400,
title={
"text": "Race of elderly people is being gradually diverged",
"subtitle": ["Racial structure for people over 65 years old in US(2006-2017)"],
"subtitleColor": "black",
"subtitleFontSize":18,
"subtitleFontWeight":"bold"
}
)
chart2 = alt.Chart(aging_his).mark_area().encode(
x="Year:N",
y="Hispanic or Latino:Q",
color=alt.Color("Race:N",
scale = alt.Scale(range=["orange"]),
legend=alt.Legend(title=None)))
chart = (chart1 + chart2).resolve_scale(color='independent')
config = chart.configure_title(
fontSize=24,
color="blue",
fontWeight="bold",
anchor="middle"
).configure_legend(
titleColor='black',
titleFontSize=13,
labelFontSize=11,
orient='right',
fillColor="#ebf5ff",
padding=10
).configure_axisX(
titleFontSize=13,
titleFontWeight="bold",
labelFontSize=11,
labelAngle=-45
).configure_axisY(
titleFontSize=13,
titleFontWeight="bold",
labelFontSize=11)
config
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3.Scatter plot for the relationship of income level and fertility(Revised)¶
Load data and preprocessing¶
Here the scatter plot below shows the relationship of median income and fertility rate, as well as the size of total population for each county. The plot seems basically scattered flat, but we might be able to see the slightly negative correlation between income and fertility rate.¶
In [0]:
chart = alt.Chart(gdf_fert, title="Relationship between fertility rate and median income"
).mark_circle(size=20).encode(
x='med_income',
y='fert_13_17',
color="region",
tooltip=['med_income', "gini_idx", 'fert_13_17']
)
alt.renderers.enable(embed_options={'theme': 'quartz'})
config = chart.configure_title(fontSize=16, anchor="middle"
).configure_legend(titleColor='black', titleFontSize=13
).configure_axisX(labelFontSize=11
).configure_axisY(labelFontSize=11)
config
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4.Slope graph for fertility rate at division level¶
Load data and preprocessing¶
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fert_vis_conc = pd.concat([fert_vis07, fert_vis17])
The slope graph below shows how fertility rate changed between 2007 and 2017 for division level. We can see that for all divisions, the fertility rate declined, and for some divisions such as East North Central and South atlantic, the fertility rate declined sharpely.¶
5.Ridgeline plot for fertility rate¶
Load data and preprocessing¶
In [0]:
urls = ["https://docs.google.com/spreadsheets/d/1sUA47Q8OrH18YI4q_DOP7OWIoHX2i8kH0fk5E4lFszE/export?format=csv&gid=1048618207",
"https://docs.google.com/spreadsheets/d/18KT-PC9IXKqYL-wd5zHHzYV4K8eZ9mgUe98XaFANMQM/export?format=csv&gid=2072191966",
"https://docs.google.com/spreadsheets/d/1p_fq_sG3nYnHdOkLVglxXF2zmT1XrPK0c-538lcFIAg/export?format=csv&gid=1688370404",
"https://docs.google.com/spreadsheets/d/1ymCdsBHgamJis3_2PK0IZS_VZvls5Umn-ng9o4S0Sro/export?format=csv&gid=119942101",
"https://docs.google.com/spreadsheets/d/1AqUeUfzuAUG5pCLJGbrysCcTxICCrHsA8rRE1ffHZpU/export?format=csv&gid=1384942932",
"https://docs.google.com/spreadsheets/d/1zvfu9Nc27aGo2ovAn2kqofv-iVXO61-n-dQ4DIrpbUs/export?format=csv&gid=2105631292",
"https://docs.google.com/spreadsheets/d/1XAXIQeLrgxgEx6CCHxWOmfgmInRxwCxu-4vw1PR8dp8/export?format=csv&gid=1064535711",
"https://docs.google.com/spreadsheets/d/1xad1XOm-J4T13rYFJ7BBeskaaCV88BAQZExqNX3aX0w/export?format=csv&gid=95139586",
"https://docs.google.com/spreadsheets/d/1pBj5q5NkzpZUKeOqFZCGb0RdzV1mvS8rw2iXWcwYKbc/export?format=csv&gid=616415653",
"https://docs.google.com/spreadsheets/d/1dZBbXEKSRqhR8Yy9_zFBAjzdw_Gyu3czYT0GNcGhrZI/export?format=csv&gid=687388009",
"https://docs.google.com/spreadsheets/d/1pReietRze4QKpMOM6dbJeJ4bdekcGgrxcaC5k4Qv8C8/export?format=csv&gid=2108461693",
"https://docs.google.com/spreadsheets/d/1KkqQ8U4vL5Tjy7Cq16FQD7xi3qnfPnyYiMYQuiJ6Hkc/export?format=csv&gid=957537605"]
dfs =[]
for url in urls:
df = pd.read_csv(url, encoding='latin-1')[1:]
dfs.append(df)
pop65_conc = pd.concat(dfs)
col_use = ["date", "HC01_EST_VC01", "HC02_EST_VC01"]
pop65_conc = pop65_conc[col_use]
pop65_conc["HC01_EST_VC01"]=pop65_conc["HC01_EST_VC01"].astype("int")
pop65_conc["HC02_EST_VC01"]=pop65_conc["HC02_EST_VC01"].astype("int")
pop65_conc["pop65_rate"] = pop65_conc["HC02_EST_VC01"]/pop65_conc["HC01_EST_VC01"]
pop65_conc["date"] =pd.to_datetime(pop65_conc["date"])
pop65_conc["year"] = pop65_conc["date"].dt.year
pop65_conc = pop65_conc.drop("date", axis=1)
pd.set_option('display.max_rows', 10000)
pop65_conc["year"] = pop65_conc["year"].astype("str")
The ridgeline plot below shows how the distribution of population proportion over age 65 changed from 2006 to 2017 for county levels. We can see that, in earlier period, the proportion of people over age of 65 was more concentrated around the mean of about 15 % and there were very limited number of counties which recorded over 20 %. Then, in recent years, we can see that the tail becomes longer and the proportion is distributed toward higher rate, and there are some part of counties which record over 24 % of population aging.¶
6. Fertility rate for counties with different race structure(New!)¶
Process data¶
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county_pts = county
county_pts["lon_lat"]= county_pts.centroid
county_pts = county_pts[["geoid", "lon_lat"]]
county_pts = county_pts.rename(columns={"lon_lat":"geometry"})
gdf_pts = county_pts.merge(df, on="geoid")
gdf_pts = gdf_pts[gdf_pts["state"] != "Alaska"]
gdf_pts = gdf_pts[gdf_pts["state"] != "Hawaii"]
gdf_ab_wh = gdf_pts[gdf_pts["above_mean_wh"]==1]
gdf_bl_wh = gdf_pts[gdf_pts["above_mean_wh"]==0]
gdf_ab_wh = gpd.GeoDataFrame(gdf_ab_wh)
gdf_bl_wh = gpd.GeoDataFrame(gdf_bl_wh)
gdf_ab_wh["region"] = gdf_ab_wh["region"].astype(str)
gdf_bl_wh["region"] = gdf_bl_wh["region"].astype(str)
Below we examine how the racial structure of each county affect the fertility rate. The above plot is for counties with higher percent of white prople than average of all counties, and the plot below is for counties with lower percent of white people. We can see that in mid-west region there are not many counties with lower percent of white people, but such counties with fewer white people seem to record relatively high fertility rate. Also, in south region, many of the counties have lower percent of white people, and most of them record average or higher level of fertility rate. On the other hand, in west region and noth-east region, some of the counties with high percent of white people show very low fertility rate.¶
In [0]:
import geoplot.crs as gcrs
alt.renderers.enable(embed_options={'theme': 'quartz'})
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
fig = plt.figure(figsize=(20,15))
proj = projection=gcrs.AlbersEqualArea(central_latitude=45.7128, central_longitude=-100.0059)
ax1 = plt.subplot(211, projection=proj)
ax2 = plt.subplot(212, projection=proj)
ax1 = gplt.pointplot(gdf_ab_wh, projection=proj, scale="fert_13_17", hue='region',
cmap='Set1',limits=(1, 20), legend=True, legend_var='scale',
edgecolor='white', linewidth=0.5, ax=ax1)
ax2 = gplt.pointplot(gdf_bl_wh, projection=proj, scale="fert_13_17", hue='region',
cmap='Set1',limits=(1, 20),legend=True, legend_var='scale',
edgecolor='white', linewidth=0.5, ax=ax2)
gplt.polyplot(contiguous_usa,edgecolor='white', facecolor='lightgray',
ax=ax1)
ax1.set_title("Fertility rate for county with high pct of white, 2013-2017",
fontdict={'fontsize': 16})
gplt.polyplot(contiguous_usa,edgecolor='white', facecolor='lightgray',
ax=ax2)
ax2.set_title("Fertility rate for county with low pct of white, 2013-2017",
fontdict={'fontsize': 16})
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7. Histogram for fertility rate with different regions(New!)¶
The histogram below shows the distribution of fertility rate for five different regions. Although absolute number of records are different, there are not big difference between each region in terms of distribution of fertility rate. For most of regions fertility rate is normally distributed around the peak of about 50 per 1000 women.¶
In [0]:
base = alt.Chart(df).properties(
width=700,
height=300,
title={
"text": ["Fertility rate distribution by region"],
"color": "black",
}
)
color_scale = alt.Scale(domain=['Midwest', 'Northeast', 'South','West', 'nan'],
range=['#98df8a', '#ff7f00','#ffed6f','#8624F5','#9d755d'])
hists = base.mark_bar(opacity=0.6, thickness=100).encode(
x=alt.X('fert_13_17',
bin=alt.Bin(maxbins=140), # step keeps bin size the same
scale=alt.Scale(domain=[0,250]),
axis=alt.Axis(title='fertility_rate', grid=False)),
y=alt.Y('count()',
stack=None,
scale=alt.Scale(domain=[0,100])),
color=alt.Color('region:N',scale=color_scale)
).configure_title(fontSize=16, anchor="middle"
).configure_legend(titleColor='black', titleFontSize=13
).configure_axisX(labelFontSize=11
).configure_axisY(labelFontSize=11)
hists
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8. Heatmap for fertility and and some variables(New!)¶
The heat map below shows how fertility rate is correlated with some demographic indicators for those areas.Here we examine the relationship with poverty rate and the percent of people who do not graduate from high school. From the map we cannot see clear correlation between fertility rate and these variables, though non-high school graduate rate might be negatively correlated with fertility rate.¶
In [0]:
import altair as alt
a = alt.Chart(df).properties(
width=300,
height=200,
title={
"text": ["Fertility rate vs poverty rate"],
"color": "black",
}
).mark_rect().encode(
alt.X('poverty_rate:Q', bin=alt.Bin(maxbins=60),
axis=alt.Axis(title='poverty_rate', grid=True)),
alt.Y('fert_13_17:Q', bin=alt.Bin(maxbins=40),
axis=alt.Axis(title='fertility_rate', grid=True)),
alt.Color("count()", scale=alt.Scale(scheme='inferno'))
)
b = alt.Chart(df).properties(
width=300,
height=200,
title={
"text": ["Fertility rate vs non-high school degree rate"],
"color": "black",
}
).mark_rect().encode(
alt.X('pct_bl_hs:Q', bin=alt.Bin(maxbins=60),
axis=alt.Axis(title='non-high schoolgrad-rate', grid=True)),
alt.Y('fert_13_17:Q', bin=alt.Bin(maxbins=40),
axis=alt.Axis(title='fertility_rate', grid=True)),
alt.Color("count()", scale=alt.Scale(scheme='inferno'),
legend=alt.Legend(title='Total Records'))
)
c = a|b
c.configure_title(fontSize=16, anchor="middle"
).configure_legend(titleColor='black', titleFontSize=13
).configure_axisX(labelFontSize=11
).configure_axisY(labelFontSize=11)
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